CONTEXT AND RELEVANCE IN COGNITIVE SEARCH
With machine learning, Attivio analyzes user behaviors and builds relevancy models that learn and improve as content, data, and user activity grows and evolves. From the user’s perspective, these techniques help in two ways: by delivering highly relevant results from search queries, and by understanding the intent behind the query itself, based on context.
Continuously Learn and Optimize
Attivio delivers increasingly relevant answers based on indexed information and behavioral interactions with that information. Such self-learning capabilities enable Attivio to present refined answers that contain sought-after information and unexpected insights.
Relevancy in Real-Time
Present the best answers at the right time, to achieve the best possible user experience. With machine learning capabilities, the system continuously detects user patterns and improves relevancy of results and recommendations, behind the scenes.
Personalize the Experience
With highly relevant results and intelligent recommendations, users get the information they need, immediately. With such an engaging experience, user adoption soars.
Machine Learning Features Include:
- Behavioral analytics from frequency, past actions or actions of similar users
- Automated classification and sentiment analysis of unstructured content
- Comparisons of unstructured content (descriptions, titles, article leads, etc.) for downstream analysis or end-user applications
- Relationships between content items based on metadata, topics, genres, concepts or entities (such as names of people, locations and organizations)
- Recommendations: contextual information based on user behaviors, preferences, and content similarities
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Attivio maintains a team of experienced and friendly Solution Architects, who routinely advise and assist organizations developing their Attivio solutions.